Effect of photoscale, interpreter bias and land type on woody crown-cover estimates from aerial photography

2007 ◽  
Vol 55 (4) ◽  
pp. 457 ◽  
Author(s):  
R. J. Fensham ◽  
R. J. Fairfax
Keyword(s):  
Aerial Photography ◽  
Vegetation Cover ◽  
Field Measurements ◽  
Woody Vegetation ◽  
Vegetation Types ◽  
Land Type ◽  
Crown Cover ◽  
Woody Cover ◽  
Land Types ◽  
Residual Deviance

Woody vegetation cover interpreted from aerial photography requires assessment against field data as the signature of woody vegetation cover may differ between photoscales, vegetation types and photo-interpreters. Measurements of aerial woody cover taken from aerial photography of four different photoscales were compared with a field dataset from Eucalyptus- and Acacia-dominated landscapes of semi-arid Queensland. Two interpreters employed a method that utilises a stereoscope and sample-point graticule for manual quantified measurements of aerial woody cover. Both interpreters generated highly significant models accounting for 77 and 78% of deviance. Photoscale appears to have a consistent effect whereby the signature of woody cover increases as the photoscale decreases from 1 : 25 000 to 1 : 80 000, although the magnitude of this effect was different between interpreters. The results suggest no substantial differences in the shape of models predicting crown cover between Acacia- and Eucalyptus-dominated land types, although the precision of the models was greater for the Acacia (90–91% of residual deviance) than for the Eucalyptus (50–56% of residual deviance) land type. The reduced accuracy in the Eucalyptus land type probably reflects the relatively diffuse crowns of the dominant trees. The models generated for this dataset are within the range of those from other calibration studies employing photography of a range of scales and methodologies. The effect of photoscale is verified between the available studies, but there may also be variations arising from methodological differences or image properties. The present study highlights the influence of photoscale and interpreter bias for assessing woody crown cover from aerial photography. Studies that employ aerial photography should carefully consider potential biases and cater for them by calibrating assessments with field measurements.

Assessing woody vegetation cover change in north-west Australian savanna using aerial photography

2003 ◽  
Vol 12 (4) ◽  
pp. 359 ◽  
Author(s):  
R. J. Fensham ◽  
R. J. Fairfax
Keyword(s):  
Structural Change ◽  
Aerial Photography ◽  
Vegetation Cover ◽  
Grazing Intensity ◽  
Field Measurements ◽  
Aerial Photographs ◽  
Woody Vegetation ◽  
North West ◽  
Land Type ◽  
Calibration Models

Models to calibrate tree and shrub cover assessed from aerial photography with field measurements were developed for a range of vegetation types in north-western Australia. The models verify previous studies indicating that woody cover can be successfully determined from aerial photography. The calibration models were applied to estimates of woody vegetation cover determined for 279 randomly located sample areas in the Ord–Victoria Rivers region using aerial photography from 1948 to 1950 and 1988 to 1997. Overstorey cover increased from a regional average of 11.5% to 13.5% and understorey cover increased from 1.3% to 2.0%. Downs, Limestone Hills and Alluvia land-types showed the most substantial increases in overstorey cover while overstorey cover in the Limestone plains land-type decreased. Relatively open structured vegetation is most susceptible to thickening. Rainfall records reveal an extreme multi-year rainfall deficit in the study area in the 1930s and relatively wet times in the 1970s and 1980s. Interpretation of a limited set of aerial photographs taken between 1964 and 1972 suggests that most of the increases in cover have occurred since this time. The study highlights the possibility that the average trend of vegetation thickening represents recovery during the relatively wet times after the 1970s. There was no relationship between structural change and a grazing intensity surrogate (distance of sample points to stock watering-points). However, the causes of structural change are undoubtedly multi-factored and the relative contributions of climate, fire and grazing vary for different landscapes and tree species.

Corrigendum to: Assessing woody vegetation cover change in north-west Australian savanna using aerial photography

2004 ◽  
Vol 13 (1) ◽  
pp. 131
Author(s):  
R. J. Fairfax ◽  
R. J. Fensham
Keyword(s):  
Structural Change ◽  
Aerial Photography ◽  
Vegetation Cover ◽  
Grazing Intensity ◽  
Field Measurements ◽  
Aerial Photographs ◽  
Woody Vegetation ◽  
North West ◽  
Land Type ◽  
Calibration Models

Models to calibrate tree and shrub cover assessed from aerial photography with field measurements were developed for a range of vegetation types in north-western Australia. The models verify previous studies indicating that woody cover can be successfully determined from aerial photography. The calibration models were applied to estimates of woody vegetation cover determined for 279 randomly located sample areas in the Ord–Victoria Rivers region using aerial photography from 1948 to 1950 and 1988 to 1997. Overstorey cover increased from a regional average of 11.5% to 13.5% and understorey cover increased from 1.3% to 2.0%. Downs, Limestone Hills and Alluvia land-types showed the most substantial increases in overstorey cover while overstorey cover in the Limestone plains land-type decreased. Relatively open structured vegetation is most susceptible to thickening. Rainfall records reveal an extreme multi-year rainfall deficit in the study area in the 1930s and relatively wet times in the 1970s and 1980s. Interpretation of a limited set of aerial photographs taken between 1964 and 1972 suggests that most of the increases in cover have occurred since this time. The study highlights the possibility that the average trend of vegetation thickening represents recovery during the relatively wet times after the 1970s. There was no relationship between structural change and a grazing intensity surrogate (distance of sample points to stock watering-points). However, the causes of structural change are undoubtedly multi-factored and the relative contributions of climate, fire and grazing vary for different landscapes and tree species.

scholarly journals Monitoring scrub weed change in the Canterbury region using satellite imagery

2007 ◽  
Vol 60 ◽  
pp. 137-140 ◽  
Author(s):  
J.D. Shepherd ◽  
J.R. Dymond ◽  
J.R.I. Cuff
Keyword(s):  
Satellite Imagery ◽  
Vegetation Cover ◽  
Satellite Image ◽  
Visual Assessment ◽  
Woody Vegetation ◽  
Spatial Change ◽  
Valid Data ◽  
Woody Cover ◽  
Landsat Satellite ◽  
Thematic Data

The spatial change of woody vegetation in the Canterbury region was automatically mapped between 1990 and 2001 using Landsat satellite image mosaics The intersection of valid data from these mosaics gave coverage of 84 of the Canterbury region Changes in woody cover greater than 5 ha were identified Of the 5 ha areas of woody change only those that were likely to have been a scrub change were selected using ancillary thematic data for current vegetation cover (eg afforestation and deforestation were excluded) This resulted in 2466 polygons of potential scrub change These polygons were rapidly checked by visual assessment of the satellite imagery and assigned to exotic or indigenous scrub change categories Between 1990 and 2001 the total scrub weed area in the Canterbury region increased by 3600 400 ha and indigenous scrub increased by 2300 400 ha

scholarly journals LANDSAT-BASED WOODY VEGETATION COVER MONITORING IN SOUTHERN AFRICAN SAVANNAHS

2016 ◽  
Vol XLI-B7 ◽  
pp. 563-567
Author(s):  
E. Symeonakis ◽  
K. Petroulaki ◽  
T. Higginbottom
Keyword(s):  
Vegetation Cover ◽  
Urban Areas ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Mitigation Measures ◽  
Management Tool ◽  
Woody Vegetation ◽  
Steady Increase ◽  
Vegetation Types

Mapping woody cover over large areas can only be effectively achieved using remote sensing data and techniques. The longest continuously operating Earth-observation program, the Landsat series, is now freely-available as an atmospherically corrected, cloud masked surface reflectance product. The availability and length of the Landsat archive is thus an unparalleled Earth-observation resource, particularly for long-term change detection and monitoring. Here, we map and monitor woody vegetation cover in the Northwest Province of South Africa, an area of more than 100,000&thinsp;km<sup>2</sup> covered by 11 Landsat scenes. We employ a multi-temporal approach with dry-season data from 7 epochs between 1990 to 2015. We use 0.5&thinsp;m-pixel colour aerial photography to collect >&thinsp;15,000 point samples for training and validating Random Forest classifications of (i) woody vegetation cover, (ii) other vegetation types (including grasses and agricultural land), and (iii) non-vegetated areas (i.e. urban areas and bare land). Overall accuracies for all years are around 80&thinsp;% and overall kappa between 0.45 and 0.66. Woody vegetation covers a quarter of the Province and is the most accurately mapped class (balanced accuracies between 0.74-0.84 for the 7 epochs). There is a steady increase in woody vegetation cover over the 25-year-long period of study in the expense of the other vegetation types. We identify potential woody vegetation encroachment 'hot-spots' where mitigation measures might be required and thus provide a management tool for the prioritisation of such measures in degraded and food-insecure areas.

A Quantitative Appraisal of Woody Shrub Encroachment in Western New South Wales.

1998 ◽  
Vol 20 (1) ◽  
pp. 26 ◽  
Author(s):  
DB Gardiner ◽  
GJ Tupper ◽  
GS Dundeon
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Vegetation Cover ◽  
New South ◽  
Geographic Information ◽  
Shrub Encroachment ◽  
Woody Vegetation ◽  
Shrub Cover ◽  
South Wales ◽  
Woody Cover

Landsat Multispectral Scanner (MSS) digital imagery was used to estimate the distribution, density and change in woody shrub cover over time in western New South Wales. The purpose of the project was to derive maps of woody cover which can be used as a basis for regional planning and property planning. Assessment of woody vegetation cover using satellite imagery enables regions which are more susceptible to shrub encroachment to be targeted for control strategies. Dry season images which had minimal green vegetation were used, because the spectral signatures of scrubby ground cover interfered with the proper classification of woody vegetation. For each region, multidate imagery was classified using a pixel unmixing algorithm to derive data sets which showed woody canopy cover. These data were then rescaled to percentage values using aerial photography sampled throughout each region. A geographic information system (GIS) was used to derive changes in woody cover between both dates and to present the data in map form. Most current woody cover in the study area occurs at less than 20% cover, whilst higher levels (40 to 80%) occur in the eastern parts of the Louth and Barnato regions. At least 20,3 10 km2 of the 120,000 km2 study area is already affected by woody vegetation cover levels of greater than 40%, which significantly reduces carrying capacity and pastoral productivity. Changes in woody cover over a 10 to 20 year period were varied. Approximately 24% (26,041 km2) was relatively stable, whilst 20% of the Barnato region had moderate decreases (1 1 to 30%) due to wildfires, and increases of 11 to 30% cover occurred on 'hard red' soils in the east. Emerging woody vegetation of less than 10% cover occurred over 1816 km2 of Sandplains and Stony Lowlands in the Louth and Barnato regions, whilst woody vegetation levels of more than 40% cover occurred in the Barnato region. Considerable 'infilling' of previously unwooded areas was noted for regions which already had high levels of woody cover. A minimal amount of prescribed clearing was apparent from the change data, which suggests that effective control of shrubs is difficult to achieve and that future scenarios will see continued encroachment. The findings suggest that the southern Louth and Barnato regions are most at risk of further shrub encroachment, and that these areas need to be targeted for shrub control. The data provide a quantitative estimate of woody shrub cover which is useful for economic assessments, as well as providing an information base upon which woody shrub management strategies can be developed. Key words: Landsat Multispectral Scanner, remote sensing, geographic information system, change detection, rangeland, monitoring, land cover.

scholarly journals Mapping trends in woody cover throughout Namibian savanna with MODIS seasonal phenological metrics and field inventory data

2019 ◽  
Author(s):  
Vladimir R. Wingate ◽  
Nikolaus J. Kuhn ◽  
Stuart R. Phinn ◽  
Cornelis van der Waal
Keyword(s):  
Environmental Factors ◽  
Field Measurements ◽  
Kendall Test ◽  
Shrub Encroachment ◽  
Spatial Extent ◽  
Woody Vegetation ◽  
Herbaceous Vegetation ◽  
Savanna Vegetation ◽  
Woody Cover ◽  
Phenological Metrics

Abstract. Woody vegetation is an integral component of savannas. Here, two main change processes alter woody vegetation, namely shrub encroachment and deforestation. Both impact a range of ecosystem services and functions across scales. Accurate estimates of change, including spatial extent, rate and drivers are lacking. This is primarily due to savanna vegetation comprising woody and herbaceous vegetation, each of which exhibit divergent phenological characteristics, and vary importantly in their response to climatic and environmental factors. This study uses phenological metrics derived from the MODIS MOD13Q1 NDVI time-series to model woody cover as a function of field measurements, and to map trends across Namibia. These metrics enhance the contrasting phenological characteristics of woody and herbaceous vegetation, and standardizes their annual response to climatic and environmental factors by integrating short term variation. Trends in woody cover are excellent indicators of shrub encroachment and deforestation. Trend significance was computed using the Mann-Kendall test, while change statistics, including the rate and spatial extent of change were derived using the Theil-Sen slope. Change was evaluated in relation to drivers including land-use, population, biomes and precipitation. An overall decrease in woody cover was identified, with the most pronounced decreases found in urban and densely populated areas. Decreases in woody cover were not homogenously distributed; losses predominated in tropical desert and dry forests, but gains were found across shrub lands.

Mapping trends in woody cover throughout Namibian savannah with MODIS seasonal phenological metrics and field inventory data

2021 ◽  
Author(s):  
Vladimir Wingate
Keyword(s):  
Vegetation Cover ◽  
Forest Cover ◽  
Validation Dataset ◽  
Anthropogenic Factors ◽  
Woody Vegetation ◽  
Mean Annual Precipitation ◽  
Herbaceous Vegetation ◽  
Modis Ndvi ◽  
Woody Cover ◽  
Phenological Metrics

&lt;p&gt;Woody vegetation is an integral component of Namibian savannahs and essential to people&amp;#8217;s livelihoods. Savannah vegetation varies in response to climatic, environmental and anthropogenic factors, moreover, its constituent plant functional types (woody and herbaceous vegetation) exhibit divergent phenological characteristics. Together, these make accurate estimates of changes in tree and shrub cover densities over time difficult to achieve. Two contrasting land degradation processes affecting woody vegetation cover are widespread: (i) the replacement of the herbaceous layer with hardy shrubs (shrub encroachment) and (ii) the loss of forest cover (deforestation). Both processes impact a range of ecosystem services, from local (i.e. local forage and timber resources) to global scales (i.e. biome carbon sequestration). To map trends in woody cover, field observations from 484 sample plots were used to model percentage woody cover as a function of seasonal phenological metrics derived from the MODIS NDVI time-series. An independent validation dataset found a RMSE of 19.73% and an R2 of 0.93%. Trends in modelled woody cover were assessed in relation to land-use, population density and mean annual precipitation. An overall declining trend was identified, with certain land-uses, including protected areas, revealing a declining trend. Significant negative trends covered 11.80% of the study area, while 9.20% underwent positive trends. Trends in woody vegetation cover are mostly unrelated to those of precipitation, except for certain areas which show high coefficients of determination, and imply the presence of predominantly herbaceous vegetation. As such, this study presents a novel method for the identification of grasslands in Namibia.&lt;/p&gt;

scholarly journals LANDSAT-BASED WOODY VEGETATION COVER MONITORING IN SOUTHERN AFRICAN SAVANNAHS

2016 ◽  
Vol XLI-B7 ◽  
pp. 563-567 ◽  
Author(s):  
E. Symeonakis ◽  
K. Petroulaki ◽  
T. Higginbottom
Keyword(s):  
Vegetation Cover ◽  
Urban Areas ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Mitigation Measures ◽  
Management Tool ◽  
Woody Vegetation ◽  
Steady Increase ◽  
Vegetation Types

Mapping woody cover over large areas can only be effectively achieved using remote sensing data and techniques. The longest continuously operating Earth-observation program, the Landsat series, is now freely-available as an atmospherically corrected, cloud masked surface reflectance product. The availability and length of the Landsat archive is thus an unparalleled Earth-observation resource, particularly for long-term change detection and monitoring. Here, we map and monitor woody vegetation cover in the Northwest Province of South Africa, an area of more than 100,000&thinsp;km&lt;sup&gt;2&lt;/sup&gt; covered by 11 Landsat scenes. We employ a multi-temporal approach with dry-season data from 7 epochs between 1990 to 2015. We use 0.5&thinsp;m-pixel colour aerial photography to collect &gt;&thinsp;15,000 point samples for training and validating Random Forest classifications of (i) woody vegetation cover, (ii) other vegetation types (including grasses and agricultural land), and (iii) non-vegetated areas (i.e. urban areas and bare land). Overall accuracies for all years are around 80&thinsp;% and overall kappa between 0.45 and 0.66. Woody vegetation covers a quarter of the Province and is the most accurately mapped class (balanced accuracies between 0.74-0.84 for the 7 epochs). There is a steady increase in woody vegetation cover over the 25-year-long period of study in the expense of the other vegetation types. We identify potential woody vegetation encroachment 'hot-spots' where mitigation measures might be required and thus provide a management tool for the prioritisation of such measures in degraded and food-insecure areas.

scholarly journals Woody Vegetation Cover, Attrition, and Patch Metrics over Eight Decades in Central Texas, United States

2021 ◽  
Vol 78 ◽  
pp. 54-66
Author(s):  
Edward C. Rhodes ◽  
Jay P. Angerer ◽  
William E. Fox ◽  
Jason R. McAlister
Keyword(s):  
United States ◽  
Vegetation Cover ◽  
Woody Vegetation ◽  
Central Texas
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